Problem
Research questions and friction points this paper is trying to address.
SNP heritability
polygenic risk score
heritability estimation
downstream effect
PRS performance
Benchmarking Heritability Estimation Strategies Across 86 Configurations and Their Downstream Effect on Polygenic Risk Score Performance
🤖 AI Summary
Estimates of SNP heritability vary substantially across methods, yet their impact on polygenic risk score (PRS) performance remains unclear. This study systematically evaluates 86 heritability estimation configurations spanning six widely used tools—GEMMA, GCTA, LDAK, DPR, LDSC, and SumHer—and integrates these estimates into the GCTA-SBLUP and LDpred2-lassosum2 frameworks to quantify, for the first time at scale, the downstream effects of heritability estimation on PRS accuracy. The results demonstrate that heritability estimates are highly sensitive to both the choice of algorithm and GRM normalization scheme, indicating they should be treated as modeling parameters rather than stable scalars. Nevertheless, the magnitude of these estimates shows virtually no association with PRS predictive performance, exhibiting near-zero and statistically non-significant correlations.
📝 Abstract
Objective: SNP heritability estimates vary substantially across estimation strategies, yet the downstream consequences for polygenic risk score (PRS) construction remain poorly characterised. We systematically benchmarked heritability estimation configurations and assessed their propagation into downstream PRS performance.
Methods: We benchmarked 86 heritability-estimation configurations spanning six tool families (GEMMA, GCTA, LDAK, DPR, LDSC, and SumHer) and ten method groups across 10 UK Biobank phenotypes, yielding 844 configuration-level estimates. Each estimate was propagated into GCTA-SBLUP and LDpred2-lassosum2 PRS frameworks and evaluated across five cross-validation folds using null, PRS-only, and full models. Eleven binary analytical contrasts were tested using Mann-Whitney U tests to identify drivers of heritability variability.
Results: Heritability ranged from -0.862 to 2.735 (mean = 0.134, SD = 0.284), with 133 of 844 estimates (15.8%) being negative and concentrated in unconstrained estimation regimes. Ten of eleven analytical contrasts significantly affected heritability magnitude, with algorithm choice and GRM standardisation showing the largest effects. Despite this upstream variability, downstream PRS test performance was only weakly coupled to heritability magnitude: pooled Pearson correlations between h^2 and test AUC were r = -0.023 for GCTA-SBLUP and r = +0.014 for LDpred2-lassosum2, with both being non-significant.
Conclusion: SNP heritability is best interpreted as a configuration-sensitive modelling parameter rather than a universally stable scalar input. Heritability estimates should always be reported alongside their full estimation specification, and downstream PRS performance is comparatively robust to moderate variation in the heritability input.
Innovation
Methods, ideas, or system contributions that make the work stand out.
heritability estimation
polygenic risk score
benchmarking
GCTA-SBLUP
LDpred2
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